Recent advances in wireless communications and micro-electro mechanical systems have enabled the development of small, low-cost sensors that possess sensing, signal processing and wireless communication capabilities. These sensors can be dispersed geometrically in large scale and be organized into networks that can monitor physical phenomena over a large field. Such distributed sensor networks can be applied to a wide range of potential applications, like large-scale reconnaissance, surveillance, environmental monitoring, anomaly detection and disaster recovery, etc.
Distributed sensing is faced with many challenges pertaining to the scarcity of power, bandwidth, and computing resources. A central problem is to find the most efficient way to deploy the sensors and use them to collect information and send data to the central data collector. Some natural questions include: how many sensors should be deployed; what degree of quantization power should each sensor possess; at what rate should data be sampled and how should they be encoded/decoded and be sent to the central collector in order to meet some distortion criteria; is the communication network formed by the sensing nodes capable of transferring the generated data rate; and more generally, is the proposed sensor network feasible. Effective design of distributed sensor networks requires fundamental understanding of the tradeoffs between sensor network parameters like number of sensors, degree of quantization at each sensor, and the distortion requirements, etc.
A standard technique for sending information from the sensors to a data fusion center would be to simply treat each sensor's observation as an independent measurement and then employ well understood techniques for its transmission, including standard quantization and channel coding. Independently of the type of channel coding performed, the standard quantization referred to in here can also be referred to as a very basic “point-to-point” coding scheme. While appropriate for some applications, this scheme becomes infeasible when the there are too many sensors sharing a resource-limited data transmission environment, such as the available wireless spectrum.
Consequently, limiting the sizes of the messages emitted by the stations without losing the quality of the sampled data is of significant interest. These stations are assumed to operate in isolation, this is, where no cooperation is allowed. A fundamental observation is that the efficiency of such networks cannot be better than a hypothetical network where such collaboration is allowed. In particular, in principle one would like to design sensor networks with performance close to a network with full collaboration; one may call the latter “joint coding” (alternatively referred to herein as “centralized coding”).
The class of techniques that attempt to capitalize on the correlations of the data to improve system performance are called “distributed coding”. Distributed coding has been the subject of many theoretical investigations in the past, for example, for a small number of sensors [T. Berger, “Multiterminal Source Coding,” Information Theory Approach to Communication, (CISM Courses and Lecture Notes No. 229), G. Longo, Ed., Wien and New York: Springer-Verlag, 1977]. More recent theoretical research addresses problems and characteristics of large sensor networks (D. Marco and E. J. Duarte-Melo and M. Liu and D. L. Neuhoff. On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data, Lecture notes in Computer Science, editor, L. J. Guibas and F. Zhao, Springer, 2003, 1-16. and P. Ishwar and A. Kumar and K. Ramchandran, On Distributed Sampling in Dense Sensor Networks: a “Bit-Conversation” Principle, IEEE Journal on Selected Areas in Communication, July, 2003). Practical research has lagged theoretical developments. The implementation of efficient distributed coding algorithms as conceived in most research relies on recent years' algorithmic breakthroughs [Slepian, D., Wolf, J K: Noiseless Coding of Correlated Information Sources. IEEE Trans. Information Theory, IT-19, 1973, pp. 471-480.]. Most practical research follows the model established by the theoretical investigations, with significant results available only for two sensors [Z. Xiong, A. Liveris, and S. Cheng, “Distributed source coding for sensor networks”, IEEE Signal Processing Magazine, vol. 21, pp. 80-94, September 2004]. However, distributed coding schemes continue to present significant practical roadblocks as they are further developed.
In some situations, it is desirable to design sensor networks with many inexpensive sensors instead of fewer more expensive ones. A central question then is whether it is feasible to design very dense sensor networks in which the sum of the total amount of information broadcasted by each sensor does not grow in an unbounded manner as we add more sensors to the environment (this is, as we make the network denser).
Recent work by Kashyap et. al. have demonstrated that it is possible to use distributed coding as well as a simple multiplexed point-to-point coding technique to attain this goal.
Accordingly, in view of the foregoing, while research continues to advance different coding techniques, a need continues to be recognized in connection with providing and implementing more practical and effective techniques.